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July/August 2006

Announcement of a publication

Mapping global urban and rural population distributions

Estimates of future global population distribution to 2015

Environment and Natural Resource Working Paper 24

Until recently, demographers and cartographers have had only two sources of information about population distribution:

  • paper maps and navigational guides showing the location of cities and towns and the administrative boundaries of countries and subnational administrative units within countries;
  • statistical data obtained from national censuses and other demographic surveys.

With this information, it has been possible for some time to present statistical information about national population size, and the location and population count of urban centres and other subnational administrative units, in map form. This combination of statistical data with information about administrative boundaries produces maps that give a geographic representation of the concerned parameters, but the usefulness of these maps is limited as they are not georeferenced and are not supported by databases that capture variations within the mapped units.

Researchers have been developing a number of techniques for mapping globally variations of parameters within countries. As these techniques have become more sophisticated, and the capacity of computers to handle very large datasets with great speed has increased, the interest in developing methods for distributing population data to the grid cells of GIS maps has also increased. Initially, GIS specialists tended to direct their efforts towards establishing the coordinates of coastlines and country boundaries, and generating georeferenced datasets for physical and environmental variables that could be derived from high-resolution aerial photography and satellite imagery. Less effort was directed towards the development of georeferenced socio-economic datasets, mainly because such data is collected by censuses and surveys and compiled for political or administrative units, and direct interpolation techniques to estimate the spatial distribution of socio-economic variables are still lacking (Clark and Rhind, 1992; Deichmann, 1996).

Despite these limitations, improvements in the quality and accessibility of georeferenced environmental data have generated growing demand for more accurate and up-to-date spatial information about the global distribution of population variables. This demand has been driven by two different concerns within the development community. One relates to the interest of demographers, sociologists and urban planners in mapping urbanization processes and defining the location and socio-economic characteristics of urban populations with more precision. The other relates to the interest of agricultural economists and rural planners in gaining access to current data showing the location and socio-economic characteristics of rural populations in relation to physical and environmental factors that affect their livelihood options and vulnerability to poverty and food insecurity.

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